NDVI time-series data reconstruction for spatial-temporal dynamic monitoring of Arctic vegetation structure

• Published in: Geo-spatial Information Science
• Main Authors: Zihong Liu, Da He, Qian Shi, Xiao Cheng
• Format: Article in Journal/Newspaper
• Language: English
• Published: Taylor & Francis Group 2024
• Subjects: Arctic vegetation structure; Normalized Difference Vegetation Index (NDVI); time series reconstruction; Mathematical geography. Cartography; GA1-1776; Geodesy; QB275-343; Arctic; Global warming; taiga; Tundra; Vorkuta
• Online Access: https://doi.org/10.1080/10095020.2024.2336602; https://doaj.org/article/a9ff0c4c2ba54481be1818f7bcc00d55

ABSTRACTSpatial-temporal dynamics monitoring of Arctic vegetation structure (i.e. distribution range of tundra and forest) is of great significance for evaluating global warming effect. Currently, time-series monitoring of Arctic vegetation structure relies primarily on the Normalized Difference Vegetation Index (NDVI), which is derived from optical remote sensing images. However, because of factors such as the long revisit period of satellites and the impact of climate, optical observations are severely lacking in the Arctic region. This results in NDVI time-series data highly discontinuous and difficult to reflect actual variations in Arctic vegetation structure, and the traditional time-series reconstruction method would usually fail for severe missing conditions. Therefore, this study developed a Time Series Reconstruction method considering Periodic Trend (TSR-PT), which is specifically for alleviating the severe missing observation condition in the Arctic region. It can separate the phenological change and trend change of the incomplete time series NDVI, and borrow the information from the neighboring unchanged years for compensate of the missing observations in current years, based on the learned inter-annual and intra-annual correlation. We explore its usability in monitoring vegetation structure variation in Vorkuta region (transition zone of tundra and taiga in the Arctic Circle) based on MODIS data. It is found that the proposed TSR-PT is able to reconstruct NDVI with reasonable phenological feature even the missing rate reaches over 70%, which is usually falsely constructed by traditional filtering or fitting method, and suppress them by 0.038 in terms of RMSE; besides, we find that since 21-century, the Arctic trees have continued to increase and encroach the original tundra ecosystem, which caused a largely Arctic vegetation structural change, and we believe the proposed method would largely promote the Arctic vegetation research.